首页|New Findings from University of Utah in the Area of Machine Learning Published ( Wasatch Fault Structure from Machine Learning Arrival Times and High-Precision E arthquake Locations)
New Findings from University of Utah in the Area of Machine Learning Published ( Wasatch Fault Structure from Machine Learning Arrival Times and High-Precision E arthquake Locations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - New research on artificial intelligenc e is the subject of a new report. According to news originating from Salt Lake C ity, Utah, by NewsRx editors, the research stated, “On 18 March 2020, a magnitud e 5.7 earthquake hit the Salt Lake valley in the state of Utah, United States.” Our news reporters obtained a quote from the research from University of Utah: “ Using a dense geophone deployment and machine learning (ML), an additional sever al thousand events were detected and located. Currently, both the mainshock and the majority of the aftershocks are suspected to have occurred on or near a deep er portion of the Salt Lake segment of the Wasatch fault-part of a large range-b ounding fault system thought to be capable of generating an Mw 7.2 earthquake. H owever, a small subset of aftershocks may have occurred on a portion of the more steeply, eastward dipping, and poorly understood West Valley fault. Unfortunate ly, the catalog locations and lack of focal mechanisms for this subset of afters hocks provide only a crude constraint on the true fault structure. To better ill uminate fault structure, we relocate the ML-generated catalog with a range of ma gnitudes from -2 to 4.6, using: (1) NonLinLoc, a nonlinear location algorithm, ( 2) source-specific station terms, and (3) waveform coherence. We further compute first-motion focal mechanisms for 68 events.”
University of UtahSalt Lake CityUtahUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMach ine Learning
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